Spare Resources Research Articles

Modular Reconfigurable Robot Distributed Computing System for Tracking Multiple Objects

A modular reconfigurable robot (MRR) is considered as a system of systems; that is, a robot control system consists of modular systems. For an MRR, onboard tracking of multiple objects is one of the most challenging tasks. This is because the task requires lots of computation resources, but an MRR system is constrained by several physical limitations. To enhance the computing performance of an MRR control system, a novel approach to organizing a distributed computing system is proposed. The key components of the proposed approach are FPGA-based specialized computational cores (SCCs). An SCC for tracking objects is designed to utilize spare resources of the MRR module's control devices. Analysis and experiments on a real testbench are performed to illustrate that the proposed approach can enhance overall computing performance of an MRR control system without adding additional components. As such, weight and dimensional parameters are saved; unnecessary power consumption and heat generation are reduced. One experimental example shows that the proposed SCC enables a Kalman filter to track objects up to 100 times faster than a comparable solution implemented with microcontrollers, while the whole distributed computing system can outperform a similar one built on high-performance CPUs.

POST: Parallel Offloading of Splittable Tasks in Heterogeneous Fog Networks

Fog computing has been promoted to support delay-sensitive applications in future Internet of Things (IoT). For a general heterogeneous fog network consisting of many dispersive fog nodes (FNs), it may well happen that some of them have delay-sensitive tasks to process, i.e., task nodes (TNs), and some have spare resources to help the TNs to process tasks, i.e., helper nodes (HNs). It remains a fundamental challenge to effectively map multiple tasks or TNs into multiple HNs to minimize every task’s service delay in a distributed manner, i.e., the multitask multihelper (MTMH) problem. The problem becomes more challenging as tasks are splittable, i.e., tasks can be divided into multiple subtasks and offloaded to multiple HNs to further reduce the service delay via the scheme similar to distributed computing, because it introduces the more complicated task division problem which results in a much larger and more complex solution space. To tackle this challenge, in this article, a generalized Nash equilibrium problem (GNEP), called parallel offloading of splittable tasks (POST), is formulated and studied thoroughly. The structural properties of the problem are characterized and thus the existence of generalized Nash equilibrium (GNE) is proven via the fixed-point theorem. Furthermore, the corresponding distributed task offloading algorithm is developed via the Gauss–Seidel-type method. The simulation results show that the proposed POST algorithm can offer much better performance in terms of the system average delay, individual delay, delay reduction ratio (DRR), and number of beneficial TNs, compared with the existing solution to the counterpart problem for nonsplittable tasks.

A survey on the service interoperability in cloud computing: Client‐centric and provider‐centric perspectives

SummaryThe vendor lock‐in is a prominent issue in cloud computing. It is caused by cloud providers who offer proprietary services, which hinders the cloud interoperability. Client‐centric interoperability enables the migration of the data and applications across clouds; it gives the clients control over their workloads and a wider range of service choices. Whereas, provider‐centric interoperability allows the providers to collaborate. Thus, providers, who have spare resources, can lend them to other providers who lack computational or storage capabilities to overcome the limitations of their local resources. In this article, we conduct a survey to differentiate between client‐ and provider‐centric interoperability solutions. We aim to provide an up‐to‐date analysis of the current tendencies and the neglected areas of the cloud interoperability field. Thus, we study the cloud service interoperability evolution through the years. Furthermore, we propose definitions for the intra‐cloud and inter‐cloud interoperability. Moreover, we propose a taxonomy to classify the cloud interoperability approaches into client‐centric and provider‐centric categories. Then, for each category, we classify the approaches based on their interoperability environment into single cloud or interconnected clouds. Finally, we analyze and compare the approaches based on multiple criteria. The study reveals the focus on the client‐centric solutions and the interoperability in interconnected clouds. We notice more interest in the data and application levels interoperability, mainly, in infrastructure as a service model. We also find that client‐centric solutions are, mostly, semantic technologies and brokers. However, provider‐centric solutions are middleware, protocols, and standards. We conclude that a generic cloud service interoperability model is needed.

An efficiency virtual resource auction mechanism based on reserve-price strategy in cloud environments

In economic-based cloud models, resource pricing mechanism plays a key role to regulate the supply and demand for resources, provides an incentive for providers, and motivates the users to trade-off between deadline, budget, and the required level of quality of service. In this paper, we formulate the problem of resource auction as a distributed constrained programming problem; then we propose an effective and efficient auction algorithm, which use reserve-price strategy to guarantee that users pay at least a given amount determined by cloud providers. In this way, we can significantly improve the efficiency of auction process as well as provide sufficient incentive for cloud providers to share their spare resources. To evaluate the proposed auction algorithm, extensive experiments are conducted in a real-world cloud platform, and the results indicate that it can significantly increase the provider revenue as well as improve the quality-of-service (QoS) satisfactory.

Dynamic Resource Management for Cloud Spot Markets

Resource management for cloud computing environments that are characterized by many layers emerges as a critical task for cloud computing providers. Such providers are compelled by the demands and strategies of stochastic customers to adopt dynamic resource management for the top-bottom scaling of the cloud resources on the basis of variable needs. Resource management in the infrastructure as a service layer relies on virtual machine (VM) characteristics, such as estimated VM classes. Given that a cloud provider offers a variety of VM classes that differ as regards the size of computing resources (e.g., central processing unit, memory, and input/output devices), optimizing cloud resources to maximize cloud revenue is a challenging dilemma. More specifically, the dynamic management of resources in cloud spot markets is confronted with various severe obstacles. In consideration of these issues, this study investigated a dynamic resource management model for cloud spot markets and put forward an efficient model that manages spare resources for the purpose of expanding cloud revenue. The model estimates the available spare capacity of a spot market, evaluates the maximum expected revenue of stagnant VMs on the basis estimated cumulative capacity, and locates the optimum VM combinations that bear complementary workloads and capacities and can coexist in a certain host. Our model also improves the understanding of cloud resource scaling and generates inferences that can be adopted in managing cloud resources for all layers as well as Reserved and On-Demand markets.

Parasite cloud service providers: on-demand prices on top of spot prices

On-demand resource provisioning and elasticity are two of the main characteristics of the cloud computing paradigm. As a result, the load on a cloud service provider (CSP) is not fixed and almost always a number of its physical resources are not used, called spare resources. As the CSPs typically don't want to be overprovisioned at any time, they procure physical resources in accordance to a pessimistic forecast of their loads and this leads to a large amount of spare resources most of the time. Some CSPs rent their spare resources with a lower price called the spot price, which varies over time with respect to the market or the internal state of the CSP. In this paper, we assume the spot price to be a function of the CSP's load. We introduce the concept of a parasite CSP, which rents spare resources from several CSPs simultaneously with spot prices and rents them to its customers with an on-demand price lower than the host CSPs' on-demand prices. We propose the overall architecture and interaction model of the parasite CSP. Mathematical analysis has been made to calculate the amount of spare resources of the host CSPs, the amount of resources that the parasite CSP can rent (its virtual capacity) as well as the probability of SLA violations. We evaluate our analysis over pricing data gathered from Amazon EC2 services. The results show that if the parasite CSP relies on several host CSPs, its virtual capacity can be considerable and the expected penalty due to SLA violation is acceptably low.

Consistency, Feasibility, and Optimality of Network Update in SDNs

In software defined networking, to maximize the network utilization, its control plane needs to frequently update the data plane via flow migration as the network conditions change dynamically. Since each switch updates its flow table independently and asynchronously, the network state transition may result in serious link congestion and packet loss if it is done directly from the initial to the final stage. Deadlocks among flows and links may also block the update process. In this paper, we pay close attention to elaborately resolving deadlocks with the help of spare paths during the network update. We prove that the feasibility of the consistent flow migration can be determined in exponential time. Furthermore, we demonstrate that even if there are multiple consistent migration plans, finding the optimal one that occupies the least leisure bandwidth resources is NP-hard. For the case in which no consistent plan is found, we introduce an efficient method to rate limit flows in order to reduce the packet loss. Extensive simulations show that our solution achieves a much smaller traffic loss rate at the cost of affordable spare link resource usage compared to prior methods.

Sharing Economy Service Experience and Its Effects on Behavioral Intention

The sharing economy allows consumers to share spare resources through online platforms and to reduce the transaction costs by using platform services. Shared enterprises use idle resources in a more intelligent manner and share the resulting benefits with consumers. This study connects the shared enterprises’ service innovations with service experience. This study examines the consumers’ energy sharing experience and its impact on their experiential values, which in turn, affects their continued sharing energy intention. In addition, this study further examines the moderating effects of social influence and sustainability on experiential value and behavioral intentions. Data were collected from 460 Taiwanese consumers. The consumers were asked to complete a survey about their experience with Gogoro energy sharing services. Structural equation modelling (SEM) was adopted to analyze the data via IBM SPSS AMOS 25.0 (Armonk, New York, U.S.). As a result, a new four-element sharing economy service experience model was suggested; the service experience had a significant and positive effect on the behavioral intention. Additionally, the experiential values had significant and positive effects on the behavioral intention. Moreover, social influence and sustainability had significant and positive moderating effects on the relationships between experiential values and behavioral intention.

Optimizing Replica Migration in Cloud Data Centers

With the wide task of massive scale Internet affiliations and epic information, the cloud has changed into the perfect condition to fulfill the continually making storage request, by flawlessness of its indisputably monster inspiration driving restriction, high accessibility and snappier access time. In this novel condition, information replication has been touted as a total reaction for improve information accessibility and decreasing access time. Regardless, replica plan structures for the most part need to move and make vast information replicas after some time between and inside server farms, understanding a gigantic overhead to the degree system weight and accessibility. Cloud Storage Providers (CSPs) offer geographically information stores giving a couple of storage classes various costs. A colossal issue looking by cloud clients is the course by which to Abuse these storage instructions to serve an software program with a length switching fantastic development preserving up be executed on its subjects at any price price. This rate incorporates personal price (i.e., storage, Put and Get costs) and capacity migration price (i.E., kind out fee). To cope with this issue, we at the beginning suggest the best isolates calculation that usage dynamic and direct programming techniques with the supposition of on hand careful records of remarkable weight on articles. To enhance such charges, we on the start suggest the appropriate affiliation that use dynamic and direct programming structures with the supposition that the the relaxation of the burden on articles is notion early. We aside from advise a light-weight heuristic framework, brought on from a mistook estimation for the Set Covering Problem, which does no longer make any supposition at the article staying rule artwork. This device collectively alternatives object replicas region, object replicas migration times, and redirection of Get (read) sport plans to cope with replicas with the goal that the money related fee of facts storage the heap up is revived even as the consumer noticed nation of no interest is fulfilled. We chart the practicality of the proposed light-weight calculation regarding cost spare resources by techniques for wide redirections utilizing Clouds test system and searches for after from Twitter..

2L-MC 3: A Two-Layer Multi-Community-Cloud/Cloudlet Social Collaborative Paradigm for Mobile Edge Computing

Mobile edge computing (MEC) is providing a promising solution for augmenting the computing and storage capacity of mobile devices by exploiting the available resources at the network edge. Among the various Internet of Things (IoT) applications, MEC could help us to narrow the gap between the requirements of IoT applications and the limited resources of IoT devices and to achieve the energy-efficient communication and computing. Importantly, the upper and edge infrastructure of cloud computing should effectively collaborate for executing the complex tasks which are requested by mobile users. In particular, community cloud computing, as a novel computational model for a specific community with common concerns (such as security, compliance, and jurisdiction), can make full use of the spare resources of networked computers to provide the facilities so that the community gains services from them. However, how to allocate the subtasks into community clouds and edge community clouds (cloudlets) is becoming a critical challenge. To tackle this challenge, this paper first proposes a two-layer multi-community-cloud/cloudlet social collaborative paradigm, called 2 L - MC 3 for MEC. Further, we formulate a problem on tasks allocation in community clouds/cloudlets by jointly taking task offloading, tasks and clouds profiles into account. To address this problem, we devise a bi-level programming model for tasks allocation. Extensive simulations are conducted for demonstrating that the proposed approach can achieve the relative global performance for satisfying the each metric comparing to the other approaches.

P2P matchmaking solution for online games

Matchmaking players is an important problem in online multiplayer games. Existing solutions employ client-server architecture, which induces several problems. Those range from additional costs associated with infrastructure maintenance to inability to play the game once servers become unavailabe (due to being under Denial of Service attack or being shut down after earning enough profit). This paper aims to provide a solution for the problem of matchmaking players on the scale of the Internet, without using a central server. In order to achieve this goal, the SelfAid platform for building custom P2P matchmaking strategies is presented. After the developer creates a service algorithm defining the matchmaking behavior specific to his/hers case, the SelfAid platform designates a number of player machines to execute the service. Furthermore, the number of designated machines adapts to the demand. SelfAid uses only spare resources of player machines, following the trend of sharing economy. A distributed algorithm is presented and its correctness is proven.

Stochastic Control of Computation Offloading to a Helper With a Dynamically Loaded CPU

Due to densification of wireless networks, there exist abundance of idling computation resources at (network) edge helpers (e.g., base stations and handheld computers). These resources can be scavenged by offloading heavy computation tasks from small Internet-of-Things (IoT) devices (e.g., sensors and wearable computing devices) in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent . Thus, it is essential to intelligently control a user (IoT device) the amounts of data for offloading and local computing so as to ensure that a computation task can be finished in time-consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU (due to the primary service). Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper CPU as Markov chains, the problem of offloading control is converted into a Markov decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For three cases ranging from zero, small to large helper buffers, the low complexity of the policies overcomes the “curse of dimensionality” in DP arising from joint consideration of channel, helper CPU, and buffer states.

Optimal Overbooking Policy for Cloud Service Providers: Profit and Service Quality

A cloud federation is a current paradigm that enables partnered cloud providers to share idle capacities during low demand periods and to purchase spare resources during demand spikes. In this research, we propose an optimal overbooking policy to maximize federation members’ profits and enhance cloud users’ experiences. The proposed policy overcomes cloud providers’ low utilization and increases their profits. Under the market-oriented cloud federation system, we use the number of idle resources in the cloud federation and the operational costs of those resources to help the provider decide on its instance exchange price. Under such a price mechanism, we develop an optimal overbooking model and identify the conditions necessary for optimal solutions. Through implementing the optimal mechanism, we observe that the proposed overbooking policy can improve a federated provider’s profits and decrease the probability of service level agreement (SLA) violation. When a provider’s capacity is relatively large and the provider adopts the proposed overbooking policy, it could achieve maximum profits and decline its SLA violation when it has unmet customer demands and there are idle resources in the cloud federation. Through establishing the cooperative game model of the cloud federation, we make a reasonable profit distribution based on Shapley value. The cloud provider’s profits and the probability of the SLA violation change as the instance price, the distribution of unserved customers, the number of federation members and penalty cost change. Compared with the other overbooking policies and no overbooking mechanisms, our research improves profits and reduces cloud provider’s overbooking risks, thereby presenting a win-win situation for both the individual providers and the cloud federation.

Decentralized and Collaborative Scheduling Approach for Active Distribution Network with Multiple Virtual Power Plants

In order to build an active distribution system with multi virtual power plants (VPP), a decentralized two-stage stochastic dispatching model based on synchronous alternating direction multiplier method (SADMM) was proposed in this paper. Through the integration of distributed energy and large-scale electric vehicles (EV) in the distribution network by VPP group, coordinative complementarity, and global optimization were realized. On the premise of energy autonomy management of active distribution network (AND) and VPP, after ensuring the privacy of stakeholders, the power of tie-line was taken as decoupling variable based on SADMM. Furthermore, without the participation of central coordinators, the optimization models of VPPs and distribution networks were decoupled to achieve fully decentralized optimization. Aiming at minimizing their own operating costs, the VPPs aggregate distributed energy and large-scale EVs within their jurisdiction to interact with the upper distribution network. On the premise of keeping operation safe, the upper distribution network formulated the energy interaction plan with each VPP, and then, the global energy optimization management of the entire distribution system and the decentralized autonomy of each VPP were achieved. In order to improve the stochastic uncertainty of distributed renewable energy output, a two-stage stochastic optimization method including pre-scheduling stage and rescheduling stage was adopted. The pre-scheduling stage was used to arrange charging and discharging plans of EV agents and output plans of micro gas turbines. The rescheduling stage was used to adjust the spare resources of micro gas turbines to deal with the uncertainty of distributed wind and light. An example of active distribution system with multi-VPPs was constructed by using the improved IEEE 33-bus system, then the validity of the model was verified.

DMR API: Improving cluster productivity by turning applications into malleable

Adaptive workloads can change on--the--fly the configuration of their jobs, in terms of number of processes. In order to carry out these job reconfigurations, we have designed a methodology which enables a job to communicate with the resource manager and, through the runtime, to change its number of MPI ranks. The collaboration between both the workload manager---aware of the queue of jobs and the resource allocation---and the parallel runtime---able to transparently handle the processes and the program data---is crucial for our throughput-aware malleability methodology. Hence, when a job triggers a reconfiguration, the resource manager will check the cluster status and return an action: an expansion, if there are spare resources; a shrink, if queued jobs can be initiated; or none, if no change can improve the global productivity. In this paper, we describe the internals of our framework and how it is capable of reducing the global workload completion time along with providing a smarter usage of the underlying resources. For this purpose, we present a thorough study of the adaptive workloads processing by showing the detailed behavior of our framework in representative experiments and the low overhead that our reconfiguration involves.

Comparison of Visual Sampling Patterns Under Simulated L2 and L0 Systems

Automated driving systems (ADS) partially or fully perform or assist with primary driving functions. According to SAE J3016 (SAE, 2016), ADS can subsume driving tasks traditionally reserved for humans, ranging from L0 (no automation) to L5 (full automation), creating varying degrees of driver interaction and responsibility. However, the literature on human-automation interaction indicates that human operators may perform at a suboptimal level when interacting with automated support systems (Parasuraman & Riley, 1997), reducing the net benefit that automation can bring while also simultaneously increasing the potential for unforeseen human errors. Yamani and Horrey (in press) proposed a theoretical framework of human-automation interaction building upon a human information-processing model (Wickens, Hollands, Banbury, & Parasuraman, 2013) that accounts for human performance when interacting with varying types and levels of automation (Parasuraman, Sheridan, & Wickens, 2000). Following the model by Yamani and Horrey (in press), we hypothesized that when the ADS is perceived to be reliable, drivers engaging with such systems (e.g. L2) would exhibit eye movements no better or worse than the drivers engaged with manual or L0 driving since the drivers allocate their reserved or spare resources to other driving-irrelevant activities such as mind wandering or task irrelevant thoughts (Yanko & Spalek, 2014). The current driving simulator study compared young drivers’ eye movements across four unique scenarios in either L0 or L2 driving systems. We asked participants to complete a three-phased skill-based training program (RAPT-3; see Unverricht, Samuel, & Yamani for review) proven effective to improve young drivers’ ability to anticipate latent hazards, immediately followed by the evaluation of their eye movements in either L0 or L2 systems using a head-mounted eye tracker and a driving simulator. Participants in the L2 condition were instructed that the system detects and mitigates existing and latent threats on the forward roadway while maintaining appropriate speed and lateral positioning for the duration of the drive. To ensure similarity between both systems, L2 participants were required to position their hands on the steering wheel and feet above the pedal. No hazards materialized in any of the four driving scenarios. Data showed similar breadths of eye movements for the drivers of the L2 and L0 systems both horizontally [M = 36.5 vs. 36.3 pixels; L2 and L0, respectively] and vertically [M = 26.9 vs. 34.5 pixels] and no difference in mean fixation durations [M = 367 vs. 333 ms for L2 and L0 conditions]. However, data indicated substantial differences between L0 and L2 conditions for number of fixations, with L2 drivers fixating less frequently than L0 drivers, [M = 687 vs. 796 fixations, t (22) = 2.53, B10 = 3.23]. The results imply that L2 drivers may sample information from the forward roadway less often than L0 drivers, suggesting the mobilization of spare resources for non-driving related tasks. Future research should examine the relationship between conveyed system reliability and attention allocation for drivers of ADS with different automation levels. In summary, the current results support Yamani and Horrey’s model and offer potential implications for the design of autonomous systems and the NHTSA automation guidelines to consider the perceived reliability of lower level ADS towards ascribing the role of the driver when the driving task is either partially or fully automated.

Combining hardware and software codecs to enhance data channels in FPGA streaming systems

This paper discusses a framework to apply compute-intensive data transforms (codecs) to data flowing through a channel between a CPU and an FPGA kernel on a heterogeneous streaming system. Codecs such as parallel compression are applied to data across a PCI-express channel in real-time, aiming to increase the effective bandwidth by using spare resources in both software and hardware. Other codecs such as encryption can be applied while minimising the loss in performance. Real-time encoding/decoding is performed by breaking up the data stream into segments for multiple threads to process different segments. This allows the interleaving of encoding, transmission and decoding on the CPU, hence the framework functions as a software pipeline. The capabilities of the framework are demonstrated using 4 compression codecs and an encryption codec on a Maxeler system. For example, 1.51x and 1.85x speed ups are observed respectively when delta compression and double to single precision floating point conversion are applied to the data being transmitted to/from an identity kernel on the FPGA. Similarly, a 1.61x speed up is observed when double-to-single precision floating point conversion is applied to data being transmitted to/from an FPGA kernel performing a Fast Fourier Transform.

DyScale: A MapReduce Job Scheduler for Heterogeneous Multicore Processors

The functionality of modern multi-core processors is often driven by a given power budget that requires designers to evaluate different decision trade-offs, e.g., to choose between many slow, power-efficient cores, or fewer faster, power-hungry cores, or a combination of them. Here, we prototype and evaluate a new Hadoop scheduler, called DyScale, that exploits capabilities offered by heterogeneous cores within a single multi-core processor for achieving a variety of performance objectives. A typical MapReduce workload contains jobs with different performance goals: large, batch jobs that are throughput oriented, and smaller interactive jobs that are response time sensitive. Heterogeneous multi-core processors enable creating virtual resource pools based on “slow” and “fast” cores for multi-class priority scheduling. Since the same data can be accessed with either “slow” or “fast” slots, spare resources (slots) can be shared between different resource pools. Using measurements on an actual experimental setting and via simulation, we argue in favor of heterogeneous multi-core processors as they achieve “faster” (up to 40 percent) processing of small, interactive MapReduce jobs, while offering improved throughput (up to 40 percent) for large, batch jobs. We evaluate the performance benefits of DyScale versus the FIFO and Capacity job schedulers that are broadly used in the Hadoop community.

(FPL 2015) Scavenger

High-level abstractions separate algorithm design from platform implementation, allowing programmers to focus on algorithms while building complex systems. This separation also provides system programmers and compilers an opportunity to optimize platform services on an application-by-application basis. In field-programmable gate arrays (FPGAs), platform-level malleability extends to the memory system: Unlike general-purpose processors, in which memory hardware is fixed at design time, the capacity, associativity, and topology of FPGA memory systems may all be tuned to improve application performance. Since application kernels may only explicitly use few memory resources, substantial memory capacity may be available to the platform for use on behalf of the user program. In this work, we present Scavenger, which utilizes spare resources to construct program-optimized memories, and we also perform an initial exploration of methods for automating the construction of these application-specific memory hierarchies. Although exploiting spare resources can be beneficial, naïvely consuming all memory resources may cause frequency degradation. To relieve timing pressure in large block RAM (BRAM) structures, we provide microarchitectural techniques to trade memory latency for design frequency. We demonstrate, by examining a set of benchmarks, that our scalable cache microarchitecture achieves performance gains of 7% to 74% (with a 26% geometric mean on average) over the baseline cache microarchitecture when scaling the size of first-level caches to the maximum.

A resource-sharing model based on a repeated game in fog computing.

With the rapid development of cloud computing techniques, the number of users is undergoing exponential growth. It is difficult for traditional data centers to perform many tasks in real time because of the limited bandwidth of resources. The concept of fog computing is proposed to support traditional cloud computing and to provide cloud services. In fog computing, the resource pool is composed of sporadic distributed resources that are more flexible and movable than a traditional data center. In this paper, we propose a fog computing structure and present a crowd-funding algorithm to integrate spare resources in the network. Furthermore, to encourage more resource owners to share their resources with the resource pool and to supervise the resource supporters as they actively perform their tasks, we propose an incentive mechanism in our algorithm. Simulation results show that our proposed incentive mechanism can effectively reduce the SLA violation rate and accelerate the completion of tasks.