Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Performance study <s> A new meta-heuristic method, called Chemical Reaction Optimization (CRO), has been proposed very recently. The method encodes solutions as molecules and mimics the interactions of molecules in chemical reactions to search the optimal solutions. The CRO method has demonstrated its capability in solving NP-hard optimization problems. In this paper, the CRO scheme is used to formulate the scheduling of Directed Acyclic Graph (DAG) jobs in heterogeneous computing systems, and a Double Molecular Structure-based Chemical Reaction Optimization (DMSCRO) method is developed. There are two molecular structures in DMSCRO: one is used to encode the execution order of the tasks in a DAG job, and the other to encode the task-to-computing-node mapping. The DMSCRO method also designs four elementary chemical reaction operations and the fitness function suitable for the scenario of DAG scheduling. In this paper, we have also conducted the simulation experiments to verify the effectiveness and efficiency of DMSCRO over a large set of randomly generated graphs and the graphs for real-world problems. <s> BIB001 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Performance study <s> Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users. <s> BIB002 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Performance study <s> Cost models are broadly used in query processing to drive the query optimization process, accurately predict the query execution time, schedule database query tasks, apply admission control and derive resource requirements to name a few applications. The main role of cost models is to produce the time needed to run the query on a specific machine. In a multi-cloud environment, this is insufficient in two aspects: firstly, the machines employed are not defined a-priori, and secondly, time estimates need to be complemented with monetary cost information, because both the economic cost and the performance are of primary importance. This work addresses these two shortcomings and aims to serve as the first proposal for a bi-objective query cost model that is suitable for queries executed over resources provided by potentially multiple cloud providers. Moreover, our approach is applicable to more generic data flow graphs, the execution plans of which do not necessarily comprise relational operators. <s> BIB003 </s> Emphasis on Cloud Optimization and Security Gaps: A Literature Review <s> Performance study <s> Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based (host-centric) Internet infrastructure. NDN can overcome the fundamental limitations of the current Internet, in particular, Denial-of-Service (DoS) attacks. However, NDN can be subject to new type of DoS attacks namely Interest flooding attacks and content poisoning. These types of attacks exploit key architectural features of NDN. This paper presents a new intelligent hybrid algorithm for proactive detection of DoS attacks and adaptive mitigation reaction in NDN. In the detection phase, a combination of multiobjective evolutionary optimization algorithm with PSO in the context of the RBF neural network has been applied in order to improve the accuracy of DoS attack prediction. Performance of the proposed hybrid approach is also evaluated successfully by some benchmark problems. In the adaptive reaction phase, we introduced a framework for mitigating DoS attacks based on the misbehaving type of network nodes. The evaluation through simulations shows that the proposed intelligent hybrid algorithm (proactive detection and adaptive reaction) can quickly and effectively respond and mitigate DoS attacks in adverse conditions in terms of the applied performance criteria. <s> BIB004
In  the throughput stepping plan is automatically derived from the particular throughput QoS goals. The Communication to computation ratio is able to be used to point out whether a task graph is computation-intensive or communication intensive BIB001 . The energy consumption and makespan linked with the resources allocated have to be taken into account in the business application process of green cloud computing. Hence, resource allocation has to be vigilantly coordinated and optimized jointly in order to accomplish an energy-efficient schedule. The amount of time taken between submission of a request and the first response is called as Response time BIB002 . Response time has been used for cloud optimization in 10.3% of the collected optimization papers. Measures such as Throughput, CPU usage, flow time, energy consumption and Communication to computation ratio are used evenly in 34.5% of the total cloud optimization papers. Flow time is represented as the sum of finalization times of all the tasks and it is expressed in Equation BIB004 . The Communication to Computation Ratio (CCR) represents as the average edge weight divided by the average node weight. The CCR is represented in Equation (10) and it indicates the significance of the communication in a task graph that determines the scheduling behaviour.  where cn(x) represents the computation cost of a node x, c(x, j) represents the computation costs from node x to node j, and (x, j) indicates the nodes number, Cj represents the time while task j is finalized. CPU usage is represented in Equation (11) and the energy consumption is calculated using Equation (12): where Tw indicates the waiting time by CPU for task and Tt indicates the total job execution time, BIB003 P represents the peak power, f represents the frequency of cloud computing nodes and c is the utilization of computation nodes. The Makespan is defined as a performance measure of the throughput of the heterogeneous grid environment. Moreover, it is a maximum time, which all the processor taken and it is calculated using next equation: The Response time is defined as the time interval between the first response and arrival time of a task. The Response time is calculated using Equation (15) and throughput is calculated using Equation (16) Meta-heuristic has been used to search optimal solutions in most of the problems in a cloud environment. To achieve better performance for most of the scheduling problems, meta-heuristic techniques are used. Recently, this approach is used to solve the NP-hard optimization problems. Table III represents the role of meta-heuristic methods for cloud optimization. Form the tabulation; it has been found that a Genetic algorithm, PSO, and its variance were used widely for cloud optimization. Among all the papers reviewed, the usage of GA and PSO in cloud optimization was about 23.5% and 23.5%, respectively. ACO algorithms are also exploited in meta-heuristic cloud optimization study, and it has been found to be 11.7%. Most of the algorithms were used for solving the problem of task scheduling and service attacks. About 23.5% work has been done to solve the task scheduling problem, and 11.7% has been used in solving the service attack problem. Within the 23.5% of task scheduling approaches, 11.7% problem is solved with GA and another 11.7% with PSO.