Best-effort Policy Research Articles

Design and Performance Analysis of RIS-Aided DCSK-WPC System With Energy Buffer

This paper proposes an energy buffer and reconfigurable intelligent surface (RIS) aided differential chaos-shift-keying (DCSK) wireless-powered communication (WPC) system, which includes an access point (AP), a RIS, and a source node equipped with an energy buffer. In such system, the source node first harvests and stores radio-frequency energy from the AP in the downlink, then uses the stored energy to transmit information to the AP in the uplink. A simple time-switching scheme for the proposed system is designed. Two different energy management policies at the source node are considered, i.e., best-effort policy (BEP) and on-off policy (OOP). The bit-error-rate (BER) expressions of the proposed system with BEP and OOP are derived over multipath Rayleigh fading channels. Results demonstrate that the proposed system is able to achieve better BER performance than the conventional DCSK-WPC system and the RIS aided DCSK-WPC buffer-less system, while keeping the non-coherent feature of the DCSK system.

Adaptive Multiuser Cooperative NOMA Scheme With Energy Buffer-Aided Near-Users for High Spectral and Energy Efficiency

In this article, a multiuser cooperative nonorthogonal multiple access (NOMA) network is considered. In order to improve their battery lifetime, the near-user (NU) Internet of Things (IoT) nodes relay information to the far-user (FU) IoT nodes using only the energy harvested from the ambience. The harvested energy at all the NU IoT nodes is stored in energy buffers. To improve the throughput performance, the best NU and best FU (BNBF) user selection scheme is employed. Moreover, unlike conventional orthogonal multiple access (OMA) networks, an adaptive NOMA network is considered in this article that switches between direct NOMA, NOMA with relaying, and OMA modes to maximize throughput. To improve accuracy and reduce the computational complexity, the energy buffer states at the NU IoT nodes are modeled using a continuous state-space Markov chain (CSMC) instead of discrete state-space Markov chain (DSMC). We derive the limiting distributions of the stored energy in energy buffers with either the best effort policy (BEP) or the on–off policy (OOP) applied for buffer energy management. Expressions for throughput are derived for both harvest-store-use (HSU) and harvest-use (HU) architectures. The Monte Carlo simulations are used to validate the derived analytical expressions.

Performance of a Cooperative Communication Network With Green Self-Sustaining Nodes

The desire to reduce carbon foot-print and avoid frequent battery replacement has increased interest in green self-sustaining wireless nodes based on energy harvesting. In this article, we analyze the performance of a two-hop cooperative network with green self-sustaining nodes. The source and relay nodes are equipped with energy buffers, and store the energy harvested from the ambience. Signal transmission is powered at both source and relay solely by the stored energy. Both fixed and adaptive-rate transmission schemes are considered, and analytical expressions are presented for throughput and ergodic rate. Unlike all previous works that use discrete-state Markov chains to model the buffer dynamics, we use the more appropriate discrete-time continuous-state space Markov chain. We consider two different energy management policies at the source and relay: best-effort policy (BEP) and on-off policy (OOP). It is observed that for fixed-rate transmission, the better choice of buffer management policy (throughput-wise) at the source and the relay buffers depends on the relay location and target rate. On the other hand, for adaptive rate transmission, using BEP policy at both the energy buffers ensures best performance. The derived analytical expressions are validated by Monte Carlo simulations.

Performance of Energy-Buffer Aided Incremental Relaying in Cooperative Networks

In this paper, we consider a two-hop cooperative network with a direct link based on an energy harvesting (EH) decode-and-forward relay. The energy-buffer equipped relay harvests energy from the ambience, and uses the harvest-store-use (HSU) architecture. Since it is known that using a discrete-state Markov chain to model the energy buffer is inaccurate even for moderate number of states, we use a discrete-time continuous-state space Markov chain instead. We derive the limiting distribution of energy for both the incremental on-off policy (IOFP) and the incremental best-effort policy (IBEP), and use them to obtain expressions for outage probability and throughput. The corresponding expressions for non-incremental signaling follow as a special case. We show that the stable buffers using IBEP harness a diversity of two as compared with those using IOFP, which attain a diversity of one. However, while buffers using IBEP are consequently more reliable than those with IOFP, their throughput performance is only marginally superior. The simulation results are presented to validate the derived analytical expressions.

Performance Analysis of Near-Optimal Energy Buffer Aided Wireless Powered Communication

In this paper, we consider a wireless powered communication system, where an energy harvesting (EH) node harvests energy from a radio frequency (RF) signal broadcasted by an access point (AP) in the downlink (DL). The node stores the harvested energy in an energy buffer and uses the stored energy to transmit data to the AP in the uplink (UL). We investigate two simple online transmission policies for the EH node, namely a best-effort policy and an on-off policy, which do not require knowledge of the EH profile nor channel knowledge. In particular, for both policies, the EH node transmits in each time slot with a constant desired power if sufficient energy is available in its energy buffer. Otherwise, the node transmits with the maximum possible power in the best-effort policy and remains silent in the on-off policy. For both policies, we use the theory of discrete-time continuous-state Markov chains to analyze the limiting distribution of the stored energy for finite- and infinite-size energy buffers. We provide this limiting distribution in closed form for a Nakagami- $m$ fading DL channel and analyze the outage probability for a Nakagami- $m$ fading UL channel. All derived analytical results are not limited to EH via RF WPT but are applicable for any independent and identically distributed EH process from e.g. solar and wind energy. Our results reveal that, for low-to-medium outage probabilities, the best-effort policy is superior to the on-off policy and the optimal UL transmit power of the EH node that minimizes the outage probability is always less than the average harvested power. The opposite behaviour is observed for high outage probabilities. Furthermore, we show that the minimum outage probability of the two proposed policies is near-optimal.

SLOOP

Most systems allocate computational resources to each executing task without any actual knowledge of the application’s Quality-of-Service (QoS) requirements. Such best-effort policies lead to overprovisioning of the resources and increase energy loss. This work assumes applications with soft QoS requirements and exploits the inherent timing slack to minimize the allocated computational resources to reduce energy consumption. We propose a lightweight progress-tracking methodology based on the outer loops of application kernels. It builds on online history and uses it to estimate the total execution time. The prediction of the execution time and the QoS requirements are then used to schedule the application on a heterogeneous architecture with big out-of-order cores and small (LITTLE) in-order cores and select the minimum operating frequency, using DVFS, that meets the deadline. Our scheme is effective in exploiting the timing slack of each application. We show that it can reduce the energy consumption by more than 20% without missing any computational deadlines.

Policy based resource allocation in IaaS cloud

In present scenario, most of the Infrastructure as a Service (IaaS) clouds use simple resource allocation policies like immediate and best effort. Immediate allocation policy allocates the resources if available, otherwise the request is rejected. Best-effort policy also allocates the requested resources if available otherwise the request is placed in a FIFO queue. It is not possible for a cloud provider to satisfy all the requests due to finite resources at a time. Haizea is a resource lease manager that tries to address these issues by introducing complex resource allocation policies. Haizea uses resource leases as resource allocation abstraction and implements these leases by allocating Virtual Machines (VMs). Haizea supports four kinds of resource allocation policies: immediate, best effort, advanced reservation and deadline sensitive. This work provides a better way to support deadline sensitive leases in Haizea while minimizing the total number of leases rejected by it. Proposed dynamic planning based scheduling algorithm is implemented in Haizea that can admit new leases and prepare the schedule whenever a new lease can be accommodated. Experiments results show that it maximizes resource utilization and acceptance of leases compared to the existing algorithm of Haizea.

Timely and Dependable Qos Adaptation in Quasi-Synchronous Systems

Most distributed communication infrastructures are not fully synchronous. They are at most quasi-synchronous: only a small part of the system can be considered as synchronous; the rest presents a more dynamic behaviour where the duration of a given activity (processing or communication) is usually relatively small, but there is a non-null probability of taking a much higher value occasionally. In these scenarios, it is very difficult (or even impossible) to always offer the desired timeliness properties. However, some applications need more than just a best-effort policy. They need at least the ability to adapt the offered quality of service (QoS) in a timely and consistent way. In the quasi-synchronous approach, the small synchronous part is used to build components able to control and validate the other parts of the system. This approach does not solve all timeliness problems per se, but can be used by applications to reach a safe state before stopping, or switch in a controlled manner between several different QoS. In this paper we explain how this architecture can be used to build communication protocols that are able to dynamically adapt their offered QoS in a timely and dependable way.

Quality of service in an information economy

Accessing and processing distributed data sources have become important factors for businesses today. This is especially true for the emerging virtual enterprises with their data and processing capabilities spread across the Internet. Unfortunately, however, query processing on the Internet is not predictable and robust enough to meet the requirements of many business applications. For instance, the response time of a query can be unexpectedly high; or the monetary cost might be too high if the partners charge for the usage of their data or processing capabilities; or the result of the query might be useless because it is based on outdated data or only on parts (rather than all) of the available data. In this work, we show how a distributed query processor can be extended in order to support quality of service (QoS) guarantees. We propose ways to integrate QoS management into the various phases of query processing: (1) Query optimization uses a multi-dimensional assessment (cost, time and result quality) of query plans, (2) query plan instantiation comprises an admission control for sub-plans, and (3) during query plan execution the QoS of the query is monitored and a fuzzy controller initiates repairing actions if needed. The goal of our work is to provide an initial step towards QoS management in distributed query processing systems and do significantly better than current distributed database systems, which are based on a best-effort policy.

차별 제어와 키 관리 기능을 통한 트리 기반의 신뢰성 있는 멀티캐스트 프로토콜의 설계 및 구현

IP 멀티캐스트는 하나의 연결을 통해 데이터를 동시에 여러 컴퓨터로 보낼 수 있게 함으로서 비디오 스트림과 같은 대용량의 데이터 전송에 효율적으로 사용될 수 있다. 하지만, IP 멀티캐스트는 UDP에 기반한 best-effort 서비스로서, TCP에서 제공하는 신뢰 전송, 혼잡 제어 및 흐름 제어를 제공하지 못하는 단점이 있다. 또한 멀티캐스트 그룹 참가자는 어떠한 인증 과정이나 절차 없이 멀티캐스트 그룹에 참가/탈퇴가 가능하고, 하나의 서브넷 내에서는 브로드캐스트를 이용하기 때문에, 데이터의 기밀을 유지하기가 어렵다는 단점이 있다. 본 논문에서는, 일-대-다 멀티캐스트 응용에 적합한 흐름 제어, 키 관리 기법을 제공하는 신뢰전송 프로토콜인 TRDMP를 제안한다. While the Internet is suffering from the massive data such as video stream, IP multicast can ease the load of the Internet by enabling one copy of digital information to be received by multiple computers simultaneously. But If multicast is based on UDP, packets are delivered using a best-effort Policy without any reliability, congestion control or flow control. Multicast group members can join or leave a multicast group at will, and multicast uses broadcast mechanism, it's very hard to keep security from unauthorized members. In this paper, we introduce a new reliable multicast protocol TRDMF proper for one-to-many multicast model with reliability, flow control, congestion control and key management.

A Reflective Model for Real-Time Applications in Open Distributed Systems

This paper presents a model for distributed applications with real-time constraints in open systems. The RTR model uses the reflective paradigm, providing realtime scheduling, control of time and synchronization constraints, and exception handling. The model adopts the CORBA standard for open systems to provide remote method calls, and handles timing constraints by using timeouts and deadlines. Therefore, the model ensures an efficient treatment of best-effort policies. Also, a prototype implemented using RTR model is described and a multimedia application is used in the prototype to express the model potentialities for real-time programming. Finally, the main characteristics of the RTR model are compared with other approaches adopted to real-time distributed applications.