Abstract Resource provisioning and allocation are two essential operations of cloud service providers (CSPs). Due to limited resources, it may sometimes not be cost‐effective for CSPs to obtain them from the market. On the other hand, releasing some less profitable resources may violate the service level agreement (SLA). This article presents an auction‐inspired model for providing/demanding resources without exchanging money among CSPs. The core idea is that CSPs do not need to pay monetary values. Instead, they exchange free resources through an auction mechanism and are encouraged to cooperate. This model, which we call bartering double auction resource allocation, helps CSPs receive the resources they need in the form of virtual machine instances with different types of functionalities. Under this mechanism, if one party violates the SLA, the other takes back its resources to avoid possible loss. The experimental results using the CloudSim simulator and the statistical analyses show that the proposed mechanism can increase CSPs' social welfare and profitability while significantly reducing SLA violations. Also, the proposed model provides four essential economic properties, that is, individual rationality , incentive compatibility , budget balancing , and economic efficiency .

Abstract Increasing resource efficiency and reducing the energy consumption of cloud data centers is critical, especially during the global CORONA virus pandemic. Virtual machines' consolidation using live migration maximizes the hosts' and the reduction of energy consumption. An increase in the host's virtual machines in the consolidation process and the dynamic workload of the virtual machines may cause the overloading in the hosts. One approach to overcome this problem is reducing the hosts' virtual machines. One crucial issue to improve the quality of the consolidation process's quality is determining the best virtual machine for the migration process. Although the selection process has lower computational complexity than other challenges (like placement and overload prediction) in the consolidation process, this issue has received less attention. This article aims to present an efficient algorithm for the selection process. We first considered five main criteria for the selection process: migration time, migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation. Then, we propose an algorithm based on analytic hierarchy process multi‐criteria decision‐making technique. Next, to determine the weight of the proposed criteria, we simulate thousands of virtual machines of the PlanetLab workloads. These weights are tunable based on the data center preferences. The results of the suggested approach results show 23% reduction in the hosts' energy consumption, 49% reduction in the number of migrations, and 18% reduction in the SLA violation compared with other techniques. So, using the proposed method may significantly reduce the overall cost of the data centers.

Abstract Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.

Summary Dynamic virtual machine (VM) consolidation is one of the emerging technologies that has been considered for low‐cost computing in cloud data centers. Quality‐of‐service (QoS) assurance is one of the challenging issues in the VM consolidation problem since it is directly affected by the increase of resource utilization due to the consolidations. In this paper, we take advantage of Markov chain models to propose a novel approach for VM consolidation that can be used to explicitly set a desired level of QoS constraint in a data center to ensure the QoS goals while improving system utilization. For this purpose, an energy‐efficient and QoS‐aware best fit decreasing algorithm for VM placement is proposed, which considers QoS objective when determining the location of a migrating VM. This algorithm employs an online transition matrix estimator method to deal with the nonstationary nature of real workload data. We also propose new policies for detecting overloaded and underloaded hosts. The performance of our proposed algorithms is evaluated through simulations. The results show that the proposed VM consolidation algorithms in this paper outperforms the benchmark algorithms in terms of energy consumption, service‐level agreement violations, and other cost factors.

Scan matching as a basic part of SLAM has a key role in localization and even mapping of mobile robots. Our innovative method named ICE matching presents a fast and accurate method to solve the challenges of this problem. Novelty in defining new features, matching mechanism and new state estimation approach congregated in this method creates a robust practical technique. Comparison with some high quality scan matching methods from different viewpoints illustrates the performance of ICE matching. It was applied besides Grid based mapping to generate fine tuned maps to compare with slam methods.

This article introduces an event driven data reporting and routing algorithm for wireless sensor networks that offers a local timing based data reporting scheme in a clustered network. When an event occurs, the nodes that has discovered the event, are supposed to report it to their respective cluster heads. All of these nodes would wait for a specific time (based on its residual energy) to send the information. However, only the one of the sensing nodes is going to send the data to the clusterhead. The most appropriate node is the one with more residual energy and it will take its tour sooner to report the data. The waiting time for each individual node is based on its residual energy and its respective cluster head’s energy. A channel access management based on CSMA is used for avoiding collision and multiple sending. The performance of this algorithm is evaluated in the sense on network lifetime and event detection reliability.

in this paper, we investigate a clustering protocol for single hop wireless sensor networks that employs a competitive scheme for cluster head selection. The proposed algorithm is named EECS-M that is a modified version to the well known protocol EECS where some of the nodes become volunteers to be cluster heads with an equal probability. In the competition phase in contrast to EECS using a fixed competition range for any volunteer node, we assign a variable competition range to it that is related to its distance to base station. The volunteer nodes compete in their competition ranges and every one with more residual energy would become cluster head. Our objective is to balance the energy consumption of the cluster heads all over the network. Simulation results show the more balanced energy consumption and longer lifetime.

Path relinking is an evolutionary approach for solving a diverse array of optimization problems. The NP-complete Steiner tree problem is a fundamental component of many real world telecommunication applications and is a critical structure underlying multicast communications. This paper describes a novel path relinking algorithm for the delay- constrained least-cost multicast routing problem. Computational results on various random generated networks indicate that our proposed path relinking algorithm results in the best overall performance with regard to total tree cost in comparison with other existing algorithms and heuristics. Keywords: Path relinking, Multicast routing, Quality of Service, Constrained Steiner tree

Current P2P systems do not fit well into mobile environments where each node have to follow multi-hop path routing and have to face unpredictable mobility. If current P2P systems are deployed on the MANET, application layer and network layer both perform different routing mechanism independent of each other, resulting in multiple layer routing redundancy. Current ad-hoc network protocols do not fulfill the P2P requirements as basically they all are flooding based protocols. Here, we propose a new controlled-flooding based approach which works at network layer and helps P2P systems to work in mobile environment efficiently.

Detecting the deadlock is one of the important problems in distributed systems. In this paper, we proposed a distributed deadlock detection algorithm. In our algorithm the chance of phantom deadlocks detection is minimized by using a new approach and some improvements to resolution of deadlocks. Our algorithm can manage the simultaneous execution of the algorithm by nodes involved in deadlocks, prevents the detection of same deadlocks and minimize the number of useless messages in simultaneous execution of the algorithm by giving the priorities to the processes. In our proposed algorithm deadlocks are resolved as soon as they detected by its unique characteristic without creating and propagating of token to erase the memories of processes.

In distributed systems which use RPC, if a failed process sent a request before failing, the receiver of this request becomes an orphan process and must roll back to undo the effects of receiving the message. There are two types of orphan: one of them mostly called "crash- orphan" in which client crashes. Another which causes orphan process is called abort-orphan in which parent's process is aborted (Baumann and Rothermel). Orphans are undesirable because they waste system resources and can make inconsistent data (Herlihy and Mckendry, 1990). In this paper initially we present two novel methods for orphan detection. Finally we compare our new methods with the older ones.