In cloud environments, load balancing task scheduling is an important issue that directly affects resource utilization. Unquestionably, load balancing scheduling is a serious aspect that must be considered in the cloud research field due to the significant impact on both the back end and front end. Whenever an effective load balance has been achieved in the cloud then good resource utilization will also be achieved. An effective load balance means distributing the submitted workload over cloud VMs in a balanced way, leading to high resource utilization and high user satisfaction. In this paper, we propose a load balancing algorithm, Binary Load Balancing -Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (Bin-LB-PSOGSA), which is a bio-inspired load balancing scheduling algorithm that efficiently enables the scheduling process to improve load balance level on VMs. The proposed algorithm finds the best Task-to-Virtual machine mapping that is influenced by the length of submitted workload and VM processing speed. Results show that the proposed Bin-LB-PSOGSA achieves better VM load average than the pure Bin-LB-PSO and other benchmark algorithms in terms of load balance level. Keywords-Gravitational search algorithm; load balancing; particle swarm optimization; task scheduling; task-to-virtual machine mapping; virtual machine loadI.
Faults and viruses often spread in networked environments by propa-10 gating from site to neighboring site. We model this process of network contamina-11 tion by graphs. Consider a graph G = (V, E), whose vertex set is contaminated 12 and our goal is to decontaminate the set V (G) using mobile decontamination 13 agents that traverse along the edge set of G. Temporal immunity τ (G) ≥ 0 is 14 defined as the time that a decontaminated vertex of G can remain continuously 15 exposed to some contaminated neighbor without getting infected itself. The im-16 munity number of G, ι k (G), is the least τ that is required to decontaminate G 17 using k agents. We study immunity number for some classes of graphs corre-18 sponding to network topologies and present upper bounds on ι1(G), in some 19 cases with matching lower bounds. Variations of this problem have been exten-20 sively studied in literature, but proposed algorithms have been restricted to mono-21 tone strategies, where a vertex, once decontaminated, may not be recontaminated. 22 We exploit nonmonotonicity to give bounds which are strictly better than those 23 derived using monotone strategies. 24 arXiv:1307.7307v1 [math.CO] 27 Jul 2013 amount of time after which it becomes contaminated. Actual decontamination is per-41 formed by mobile cleaning agents which which move from host to host over network 42 connections. 43 1.1 Previous Work 44 Graph Search. The decontamination problem considered in this paper is a variation of 45 a problem extensively studied in the literature known as graph search. The graph search 46 problem was first introduced by Breish in [5], where an approach for the problem of 47 finding an explorer that is lost in a complicated system of dark caves is given. Parsons 48 ([20][21]) proposed and studied the pursuit-evasion problem on graphs. Members of 49 a team of searchers traverse the edges of a graph in pursuit of a fugitive, who moves 50 along the edges of the graph with complete knowledge of the locations of the pursuers. 51 The efficiency of a graph search solution is based on the size of the search team. Size of 52 smallest search team that can clear a graph G is called search number, and is denoted in 53 literature by s(G). In [19], Megiddo et al. approached the algorithmic question: Given 54 an arbitrary G, how should one calculate s(G)? They proved that for arbitrary graphs, 55 determining if the search number is less than or equal to an integer k is NP-Hard. They 56 also gave algorithms to compute s(G) where G is a special case of trees. For their 57 results, they use the fact that recontamination of a cleared vertex does not help reduce 58 s(G), which was proved by LaPaugh in [16]. A search plan for G that does not involve 59 recontamination of cleared vertices is referred to as a monotone plan.60 Decontamination. The model for decontamination studied in literature is defined as 61 follows. A team of agents is initially located at the same node, the homebase, and all 62 the other nodes are contaminated. A decontamination strategy co...
Android is currently the most popular smartphone operating system in use, with its success attributed to the large number of applications available from the Google Play Store. However, these contain issues relating to the storage of the user's sensitive data, including contacts, location, and the phone's unique identifier (IMEI). Use of these applications therefore risks exfiltration of this data, including unauthorized tracking of users' behavior and violation of their privacy. Sensitive data leaks are currently detected with taint analysis approaches. This paper addresses these issues by proposing a new static taint analysis framework specifically for Android platforms, termed "B-Droid". B-Droid is based on static taint analysis using a large set of sources and sinks techniques, side by side with the fuzz testing concept, in order to detect privacy leaks, whether malicious or unintentional by analyses the behavior of Applications Under Test (AUTs). This has the potential to offer improved precision in comparison to earlier approaches. To ensure the quality of our analysis, we undertook an evaluation testing a variety of Android applications installed on a mobile after filtering according to the relevant permissions. We found that B-Droid efficiently detected five of the most prevalent commercial spyware applications on the market, as well as issuing an immediate warning to the user, so that they can decide not to continue with the AUTs. This paper provides a detailed analysis of this method, along with its implementation and results.
In the last decade, mobile learning applications have attracted a significant amount of attention. Huge investments have been made to develop educational applications that can be implemented on mobile devices. However, mobile learning applications have some limitations, such as storage space and battery life. Cloud computing provides a new idea to solve some limitations of mobile learning applications. However, there are other limitations, like scalability, that must be solved before mobile cloud learning can become completely operational. There are two main problems with scalability. The first occurs when the application server's performance declines due to an increase in the number of requests, which affects usability. The second is that a decrease in the number of requests makes most application servers idle and therefore wastes money. These two problems can be avoided or minimized by provisioning autoscaling techniques that permit the acquisition and release of resources dynamically to accommodate demand. In this paper, we propose an intelligent neuro-fuzzy reinforcement learning approach to solve the scalability problem in mobile cloud learning applications, and evaluate the proposed approach against some of the existing approaches via MATLAB. The large state space and long training time required to find the optimal policy are the main problems of reinforcement learning. We use fuzzy Q-learning to solve the large state space problem by grouping similar variables in the same state; there is then no need to use large look-up tables. The use of parallel learning agents reduces the training time needed to determine optimal policies. The experimental results prove that the proposed approach is able to increase learning speed and reduce the training time needed to determine optimal policies.
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