Individual Servers Research Articles

Deep reinforcement learning-based resource scheduling for energy optimization and load balancing in SDN-driven edge computing

Traditional techniques for edge computing resource scheduling may result in large amounts of wasted server resources and energy consumption; thus, exploring new approaches to achieve higher resource and energy efficiency is a new challenge. Deep reinforcement learning (DRL) offers a promising solution by balancing resource utilization, latency, and energy optimization. However, current methods often focus solely on energy optimization for offloading and computing tasks, neglecting the impact of server numbers and resource operation status on energy efficiency and load balancing. On the other hand, prioritizing latency optimization may result in resource imbalance and increased energy waste. To address these challenges, we propose a novel energy optimization method coupled with a load balancing strategy. Our approach aims to minimize overall energy consumption and achieve server load balancing under latency constraints. This is achieved by controlling the number of active servers and individual server load states through a two stage DRL-based energy and resource optimization algorithm. Experimental results demonstrate that our scheme can save an average of 19.84% energy compared to mainstream reinforcement learning methods and 49.60% and 45.33% compared to Round Robin (RR) and random scheduling, respectively. Additionally, our method is optimized for reward value, load balancing, runtime, and anti-interference capability.

Enhancing Industrial IoT Security: Utilizing Blockchain-Assisted Deep Federated Learning for Collaborative Intrusion Detection

The Industrial Internet of Things (IIoT) is a rapidly evolving features with multiple applications, including critical infrastructure. Privacy policies are required to preserve the protection of user data in the threat intelligence community. Blockchain is a modern technology which used recently to provide more secure storage and efficiency. In this research, Blockchain Assisted Deep Federated Learning (BC_DFL) system is used to detect intruders. The three key processes used in the proposed intrusion detection architecture are data collection, pre-processing and intrusion detection. Data normalization, reduction, cleaning and transformation are used in pre-processing to remove extraneous information and improve data quality. This pre-processed data is sent to the Blockchain Assisted Deep Federated Learning (BC_DFL) system for intrusion detection. To detect intruders, the federated learning-based Capsule Auto-Encoder (FL_CAE) architecture first learns the properties from the inputs. Blockchain technology (BCTech) is not only used for storage but also improves security by eliminating the possibility of threatening node and individual server failure. The ToN_IoT and UNSW-NB15 data sets are used in the implementation and performance evaluation study. The proposed model is evaluated using existing in the Results section. In the UNSW-NB15 dataset, proposed model achieved an accuracy, precision, recall and F1 score of 97.26%, 97.28%, 96.89% and 96.96% respectively as well as in ToN_IoT data set proposed model achieved an accuracy, precision, recall and F1 score of 95.74%, 99.54 %, 99.49% and 99.24%, respectively. The execution of the proposed approach takes 2.31 seconds in the UNSW-NB15 dataset and 1.66 seconds in the ToN_IoT dataset. Blockchain offers a transparent and impenetrable ledger for transaction recording and verification. Within the framework of cooperative intrusion detection, it guarantees a secure and reliable exchange of threat intelligence and detection models between various users of IIoT ecosystem.

A Comprehensive survey of Load Balancing Techniques: From Classic Methods to Modern Algorithms

Now-a-days, cloud computing has become a cornerstone of modern technology, driving innovation, efficiency and accessibility across various industries and application. Distributed computing solves the difficulty of using distributed autonomous machines and communicating with each other over a network. Cloud computing provides clients with a range of services and capabilities that enhances productivity, accessibility and scalability while reducing the need for extensive hardware and infrastructure investments. When interest in distributed computing rises, it implies that more individuals, businesses, and organizations are exploring, adopting, and implementing distributed computing solutions. This surge in interest leads to increase in data traffic. There are two solutions for the issue of increase in data traffic, one is to server optimization (or) server performance enhancement (upgrade single server to a high performance server) but upgraded server may exceed its capacity (overload).Another one is multi servers. Ulti-server configurations are common in scenarios where the demands of an application or service exceed the capabilities of a single server, and the distribution of tasks across multiple servers is necessary for optimal performance and reliability. When there are multiple servers, there is an issue i.e. Load Adjusting. [1] Load balancing is one of the critical issues in cloud computing. In a cloud environment, where resources are often dynamically allocated and distributed, load balancing plays a central role in managing workloads efficiently. Load balancing in cloud computing is a technique used to distribute computing workloads and network traffic across multiple servers or resources within a cloud environment. The primary goal of load balancing is to optimize resource utilization, prevent individual servers from becoming overloaded, and ensure that the overall system can handle varying levels of demand efficiently. Here we also discussed about the hybrid of Cat and Mouse Optimization and Grey Wolf Optimization algorithms. This paper refers to Cloud Computing, Load balancing techniques, Load balancing algorithms.

SIDS: A federated learning approach for intrusion detection in IoT using Social Internet of Things

The Internet of Things (IoT) ecosystem needs Intrusion Detection Systems (IDS) to mitigate cyberattacks and exploit security vulnerabilities. Over the past years, utilizing machine learning in IDSs has gained a lot of attention. However, in many current works, the training data from different locations should be collected in a central server to be used in the learning process. This data-sharing procedure increases concerns regarding data privacy and decreases the data holders’ motivation to participate in the learning process. The use of distributed learning models has been considered a solution to overcome concerns related to privacy. However, these distributed learning models are vulnerable in the presence of untrusted nodes that can participate in the learning process and deteriorate performance. In this paper, we propose SIDS (Social Intrusion Detection System), a trust-oriented federated learning approach for intrusion detection in IoT that utilizes the Social Internet of Things (SIoT). The proposed approach leverages the social relationships among the objects in a system to provide a privacy-preserving collaborative mechanism for detecting intrusions in IoT environments. The experimental results show the proposed solution outperforms the learning models on individual servers while providing a privacy-preserving and trustable environment for collaboration.

Robustness of Cloud Manufacturing System Based on Complex Network and Multi-Agent Simulation

Cloud manufacturing systems (CMSs) are networked, distributed and loosely coupled, so they face great uncertainty and risk. This paper combines the complex network model with multi-agent simulation in a novel approach to the robustness analysis of CMSs. Different evaluation metrics are chosen for the two models, and three different robustness attack strategies are proposed. To verify the effectiveness of the proposed method, a case study is then conducted on a cloud manufacturing project of a new energy vehicle. The results show that both the structural and process-based robustness of the system are lowest under the betweenness-based failure mode, indicating that resource nodes with large betweenness are most important to the robustness of the project. Therefore, the cloud manufacturing platform should focus on monitoring and managing these resources so that they can provide stable services. Under the individual server failure mode, system robustness varies greatly depending on the failure behavior of the service provider: Among the five service providers (S1-S5) given in the experimental group, the failure of Server 1 leads to a sharp decline in robustness, while the failure of Server 2 has little impact. This indicates that the CMS can protect its robustness by identifying key servers and strengthening its supervision of them to prevent them from exiting the platform.

Spoofing Attack Mitigation in Address Resolution Protocol (ARP) and DDoS in Software-Defined Networking

Software Defined Networking (SDN) shows network operations to be performed for efficient network operations. Due to the increase in network devices, the percentage of attacks is also increased, and it is challenging to provide defense against such attacks. In SDN, the control plan is separated from the data plane. The control plan is implemented using some central devices called SDN controllers. In SDN Address Resolution Protocol (ARP), spoofing and Distributed Denial of Services (DDoS) attacks are carried out on an enormous scale. These are commonly launched attacks in SDN. Due to these attacks, the network performance is down, and network services are dead. This paper proposed a new auto detection methodology to detect ARP and DDoS attacks and mitigate SDN networks from these attacks. Additionally, we implemented two algorithms: one for flow rules and the second for attack detection. An individual server was installed to check the malicious traffic installation. We present the new forward flooding rules to detect and mitigate attacks. The experiments are performed using LINUX-based network implementation. Our proposal successfully improves network security and enhances network efficiency.

ReAD: Reliability aware data

As transistor technology scales beyond 32 nm, the reliability of the processors and servers reduces at an exponential rate. In recent times, the qualification of reliability of servers has reduced from 5 years to 3 years. With this current trend, the lifetime of servers can be expected to shrink further. In a cloud data center, with hundreds and hundreds of servers, the ill effect of shorter lifetime reliability will be magnified. Reliability of servers influences the performance, cost and power efficiency of a cloud data center. In this paper, a cost-effective technique to continuously monitor the reliability of servers and scheduling tasks based on these reliability values is proposed. The proposed technique monitors the hardware parameters for the current executing workload to calculate the reliability metrics. The reliability metric used in ReAD is MTTF (Mean Time to Fail) and this is calculated for individual servers. The load balancer reads the MTTF of individual servers and performs the load balance, considering reliability as one of the parameters. The proposed technique is non-invasive in nature and does not require the server to go into a special test mode. This saves the testing time. The experimental analysis shows on an average 25% improvement in Server Usage Metric with the minimum improvement around 5% and the maximum is around 50%.

HILIGT, upper limit servers I—Overview

The advent of all-sky facilities, such as the Neil Gehrels Swift observatory, the All Sky Automated Search for Supernovae (ASAS-SN), eROSITA and Gaia has led to a new appreciation of the importance of transient sources in solving outstanding astrophysical questions. Identification and catalogue cross-matching of transients has been eased over the last two decades by the Virtual Observatory but we still lack a client capable of providing a seamless, self-consistent, analysis of all observations made of a particular object by current and historical facilities. HILIGT is a web-based interface which polls individual servers written for XMM-Newton , INTEGRAL and other missions, to find the fluxes, or upper limits, from all observations made of a given target. These measurements are displayed as a table or a time series plot, which may be downloaded in a variety of formats. HILIGT currently works with data from X-ray and Gamma-ray observatories.

Graphic modeling in Distributed Autonomous and Asynchronous Automata (DA3)

Automated verification of distributed systems becomes very important in distributed computing. The graphical insight into the system in the early and late stages of the project is essential. In the design phase, the visual input helps to articulate the collaborative distributed components clearly. The formal verification gives evidence of correctness or malfunction, but in the latter case, graphical simulation of counterexample helps for better understanding design errors. For these purposes, we invented Distributed Autonomous and Asynchronous Automata (DA3), which have the same semantics as the formal verification base—Integrated Model of Distributed Systems (IMDS). The IMDS model reflects the natural characteristics of distributed systems: unicasting, locality, autonomy, and asynchrony. Distributed automata have all of these features because they share the same semantics as IMDS. In formalism, the unified system definition has two views: the server view of the cooperating distributed nodes and the agent view of the migrating agents performing distributed computations. The automata have two formally equivalent forms that reflect two views: Server DA3 for observing servers exchanging messages, and Agent DA3 for tracking agents, which visit individual servers in their progress of distributed calculations. We present the DA3 formulation based on the IMDS formalism and their application to design and verify distributed systems in the Dedan environment. DA3 formalism is compared with other concepts of distributed automata known from the literature.

Skill Level in Tennis Serve Return Is Related to Adaptability in Visual Search Behavior.

Analyzing visual search strategies in tennis is primarily focused on studying relationships between visual behavior and tennis performance. However, diverse movement characteristics among different servers suggest the importance of adjusting the visual search strategies of an individual while playing against different opponents. The aim of this study was to analyze whether visual search strategies can be attributed to the individual server and the returning player during the tennis serve return or return performance. Seventeen tennis players were enrolled in this study (five international players and 12 national players) producing a sample of 1,020 returns measured with mobile eye trackers. The random forest machine learning model was used to analyze the ability to classify the returning player [area under the curve (AUC): 0.953], individual server (AUC: 0.686), and return performance category (AUC: 0.667) based on the location and duration of the focal vision fixation. In international tennis players, the higher predictability of the server was observed as compared with national level players (AUC: 0.901 and 0.834, respectively). More experienced tennis players presented with a higher ability to adjust their visual search strategies to different servers. International players also demonstrated anticipatory visual behavior during the tossing hand movement and superior information pickup during the final phases of the stroke of a server.

Slow Replica and Shared Protection: Energy-Efficient and Reliable Task Assignment in Cloud Data Centers

With the explosive growth in the scale of cloud computing infrastructures, reliability and energy efficiency have become important concerns considering the great complexity of cloud data centers. There is an urgent need for efficient task assignment that can dispatch tasks to appropriate cloud data center servers, which is critical to achieve reliability and energy efficiency in current cloud data centers. Most of the research on task assignment focuses on only one of the objectives of reliability and energy efficiency, while the two objectives are intrinsically conflicting with each other. In this paper, we deal with the problem of task assignment in data centers, with the objective of minimizing the energy consumption while providing failure tolerance to task execution failure. We propose a reliability-aware and energy-efficient task replica assignment algorithm based on running task replicas at a low speed and enabling multiple task replicas to share the same server resources. Each task in a job processed by the cloud computing platform has two instances: main task and task replica (shadow). Each main task runs on an individual server, and the task replica associated with the main task is assigned on a different server. The main tasks run at the full server speed, while the task replicas run at a lower rate than the main tasks. The task replicas can be mapped onto dedicated backup servers or be assigned to the servers on which the main tasks are running. Multiple task replicas can share the same server resources to reduce the number of servers required. We conduct experiments through simulations. Experimental results demonstrate that the proposed algorithm can effectively reduce the energy consumption, while achieving a good balance between the number of servers used and job completion time.

Capacity-Achieving Private Information Retrieval Schemes From Uncoded Storage Constrained Servers With Low Sub-Packetization

This paper investigates reducing sub-packetization of capacity-achieving schemes for uncoded Storage Constrained Private Information Retrieval (SC-PIR) systems. In the SC-PIR system, a user aims to download one out of K files from N servers while revealing nothing about the identity of the requested file to any individual server, in which the K files are stored at the N servers in an uncoded form and each server can store up to μK equivalent files, where μ is the normalized storage capacity of each server. We first prove that there exists a capacity-achieving SC-PIR scheme for a given storage design if and only if all the packets are stored exactly at M\triangleq μN servers for μ such that M=μN ∈ {2,3,...,N}. Then, the optimal sub-packetization for capacity-achieving linear SC-PIR schemes is characterized as the solution to an optimization problem, which is typically hard to solve since it involves non-continuous indicator functions. Moreover, a new notion of array called Storage Design Array (SDA) is introduced for the SC-PIR system. With any given SDA, an associated capacity-achieving SC-PIR scheme is constructed. Next, the SC-PIR schemes that have equal-size packets are investigated. Furthermore, the optimal equal-size sub-packetization among all capacity-achieving linear SC-PIR schemes characterized by Woolsey et al. is proved to be \frac N(M-1)gcd(N,M), which is achieved by a construction of SDA. Finally, by allowing unequal size of packets, a greedy SDA construction is proposed, where the sub-packetization of the associated SC-PIR scheme is upper bounded by \frac N(M-1)gcd(N,M). Among all capacity-achieving linear SC-PIR schemes, the sub-packetization is optimal when min{M,N-M}|N or M=N, and within a multiplicative gap \frac min{M,N-M}gcd(N,M) of the optimal one in general. In particular, for the special case N=d·M±1 where the positive integer d ≥ 2, we propose another SDA construction to obtain lower sub-packetization.

Analysis and enhancement of secure three-factor user authentication using Chebyshev Chaotic Map

The most popular solution for a variety of business applications such as e-banking and e-healthcare is the multi-server environment. The user registration of individual servers is not the primary concern in such an environment. Here, the user can get various services from different servers by registering him/her under one server. To get secure services through this environment, the authenticity of users and servers are crucial. In this observation, the smart card based biometric authentication system is well popular and easy to use. This paper delineates that the security flaws can be found in a trusted authentication scheme proposed by Chatterjee et al. (2018). Besides, this work proposes an enhanced authentication scheme namely, asymmetric encryption based secure user authentication (ASESUA) to eliminate the drawbacks of Chatterjee et al.’s scheme. The formal security analysis of ASESUA has been done with the help of random oracle model and verified by the well popular AVISPA tool. The analysis shows that ASESUA performs better concerning security, communication cost, and computation cost than other related existing schemes.

Scaling & fuzzing: Personal image privacy from automated attacks in mobile cloud computing

The mobile cloud computing (MCC) paradigm provides a range of useful services to smart phone users and enhances the user experience significantly. But, as MCC requires the data to be offloaded to an external server, there are serious concerns regarding the privacy of the users’ personal data such as images. For instance, a cloud server could perform image segmentation on user images to extract interesting artifacts such as restaurants, user’s clothing preferences, tourist locations, participation in social events and so on, which characterize the user’s personal life. The leakage of such private information could lead to milder consequences like targeted advertising or more serious consequences like identity theft. In this work, to protect the privacy of user images, we describe a privacy-preserving image filtering for mobile cloud computing that protects against automated inference attacks based on techniques like image segmentation. The key intuition of our approach is to leverage the inherent properties of the discrete Fourier transform (DFT), which transforms each image pixel into a complex value real and imaginary parts which can be processed independently. By dividing the image in this manner, we are able to process distinct parts of the image on different non-colluding servers and aggregate the results at the client. Furthermore, to prevent information leakage at individual servers, we obfuscate the data sent to any given server using an efficient reversible transformation. We prove our approach to be secure under the semi-honest model and non-colluding servers where at least one server does not collude with the rest of the servers. In comparison to the existing paradigm of outsourced privacy preserving computation, i.e., processing encrypted data using homomorphic encryption, our approach employs easy-to-implement obfuscation techniques without any key management overhead at the client. Using experimental evaluation as well as information theoretic leakage evaluation, we show that our approach is efficient and suitable for users of mobile devices.

Synergy via Redundancy: Adaptive Replication Strategies and Fundamental Limits

The maximum possible throughput (or the rate of job completion) of a multi-server system is typically the sum of the service rates of individual servers. Recent work shows that launching multiple replicas of a job and canceling them as soon as one copy finishes can boost the throughput, especially when the service time distribution has high variability. This means that redundancy can, in fact, create synergy among servers such that their overall throughput is greater than the sum of individual servers. This work seeks to find the fundamental limit of the throughput boost achieved by job replication and the optimal replication policy to achieve it. While most previous works consider upfront replication policies, we expand the set of possible policies to delayed launch of replicas. The search for the optimal adaptive replication policy can be formulated as a Markov Decision Process, using which we propose two myopic replication policies, MaxRate and AdaRep, to adaptively replicate jobs. In order to quantify the optimality gap of these and other policies, we derive upper bounds on the service capacity, which provide fundamental limits on the throughput of queueing systems with redundancy.

Throughput-Conscious Energy Allocation and Reliability-Aware Task Assignment for Renewable Powered In-Situ Server Systems

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In-situ</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InS</i> ) server systems are typically deployed in special environments to handle <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InS</i> workloads which are generated from environmentally sensitive areas or remote places lacking modern power supply infrastructure. This special operating environment of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InS</i> servers urges such systems to be powered by renewable energy. In addition, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InS</i> systems are vulnerable to soft errors due to the harsh environments they deploy. This article tackles the problem of allocating harvested energy to renewable powered servers and assigning the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InS</i> workloads to these servers for optimizing throughput of both the overall system and individual servers under energy and reliability constraints. We perform the energy allocation based on system state. In particular, for systems in low energy state, we propose a game theoretic approach that models the energy allocation as a cooperative game among multiple servers and derives a Nash bargaining solution. To meet the reliability constraint, we analyze the reliability optimality of assigning tasks to multiple servers and design a reliability-aware task assignment heuristic based on the analysis. Experimental results show that with a small time overhead, the proposed energy allocation approach achieves a high throughput from perspectives of both the overall system and individual servers, and the proposed task assignment approach ensures an increased system reliability.

Password-Authenticated Decentralized Identities

Password-authenticated identities, where users establish username-password pairs with individual servers and use them later on for authentication, is the most widespread user authentication method over the Internet. Although they are simple, user-friendly, and broadly adopted, they offer insecure authentication and position server operators as trusted parties, giving them full control over users’ identities. To mitigate these limitations, many identity systems have embraced public-key cryptography and the concept of decentralization. All these systems; however, require users to create and manage public-private keypairs. Unfortunately, users usually do not have the required knowledge and resources to properly handle cryptographic secrets, which arguably contributed to the failures of many end-user public-key infrastructures (PKIs). In fact, as of today, no end-user PKI, able to authenticate users to web servers, has a significant adoption rate. In this paper, we propose Password-authenticated Decentralized Identities (PDIDs), an identity and authentication framework where users can register their self-sovereign username-password pairs and use them as universal credentials. Our system provides a global namespace, human-meaningful usernames, and resilience against username collision attacks. A user’s identity can be used to authenticate the user to any server without revealing that server anything about the password, such that no offline dictionary attacks are possible against the password. We analyze PDIDs and implement it using existing infrastructures and tools. We report on our implementation and evaluation.

A GPU-Accelerated In-Memory Metadata Management Scheme for Large-Scale Parallel File Systems

Driven by the increasing requirements of high-performance computing applications, supercomputers are prone to containing more and more computing nodes. Applications running on such a large-scale computing system are likely to spawn millions of parallel processes, which usually generate a burst of I/O requests, introducing a great challenge into the metadata management of underlying parallel file systems. The traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner, which will inevitably confront with serious network and consistence problems. This work instead pursues to enhance the metadata performance in the scale-up manner. Specifically, we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in parallel. Our proposal designs a novel metadata server architecture, which employs CPU to interact with file system clients, while offloading the computing tasks about metadata into GPU. To take full advantages of the parallelism existing in GPU, we redesign the in-memory data structure for the name space of file systems. The new data structure can perfectly fit to the memory architecture of GPU, and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests concurrently. We implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal, and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50% under typical metadata operations. The superiority is strengthened further on high concurrent scenarios, e.g., the high-performance computing systems supporting millions of parallel threads.

DeMSF: a Method for Detecting Malicious Server Flocks for Same Campaign

Nowadays, cybercriminals tend to leverage dynamic malicious infrastructures with multiple servers to conduct attacks, such as malware distribution and control. Compared with a single server, employing multiple servers allows crimes to be more efficient and stealthy. As the necessary role infrastructures play, many approaches have been proposed to detect malicious servers. However, many existing methods typically target only on the individual server and therefore fail to reveal inter-server connections of an attack campaign.In this paper, we propose a complementary system, deMSF, to identify server flocks, which are formed by infrastructures involved in the same malicious campaign. Our solution first acquires server flocks by mining relations of servers from both spatial and temporal dimensions. Further we extract the semantic vectors of servers based on word2vec and build a textCNN-based flocks classifier to recognize malicious flocks. We evaluate deMSF with real-world traffic collected from an ISP network. The result shows that it has a high precision of 99% with 90% recall.

The Asymptotic Capacity of Private Search

The private search problem is introduced, where a dataset comprised of $L$ i.i.d. records is replicated across $N$ non-colluding servers, each record takes values uniformly from an alphabet of size $K$, and a user wishes to search for all records that match a privately chosen value, without revealing any information about the chosen value to any individual server. The capacity of private search is the maximum number of bits of desired information that can be retrieved per bit of download. The asymptotic (large $K$) capacity of private search is shown to be $1-1/N$, even as the scope of private search is further generalized to allow approximate (OR) search over a number of realizations that grows with $K$. The results are based on the asymptotic behavior of a new converse bound for private information retrieval with arbitrarily dependent messages.