Cloud computing is a paradigm to provide services to end-users through the Internet. The availability of services to end-users is dependent on various factors such as the availability of computing resources as well as the number of users to access those services. To manage the real-time fluctuating workload cloud providers use elasticity mechanisms. Elasticity is one of the important characteristics of cloud computing that dynamically allocates computing resources to manage the fluctuating workload. The failure of allocation/de-allocation of computing resources at the right moment leads to SLA violation, degradation of services performance, maximum power consumption, minimum throughput, and maximum response time. To address these challenges, we have proposed a hybrid approach to perform horizontal elasticity. The proposed approach uses both reactive and proactive approaches for provisioning/de-provisioning of computing resources. The simulation results of the proposed model show that performance of system has improved in terms of CPU utilization, response time, and throughput.

Highly demanding services require an appropriate amount of resources tomanage the fluctuating workload in cloud environment, which is a challenging task forcloud service provides over the Internet. Cloud providers offer these services to enduserwith pay and use model, such as utility computing. The services are offered toend-user by a cloud provider in a shareable fashion over Infrastructure-as-a-Service.So, IaaS is a type of computing service on which third parties host their application onvirtualized platforms, such as either VMs or Containers. Whenever some containers areoverloaded or under-loaded, it may cause SLA violation, degrade performance, cosumemaximum energy, and also cause minimum throughput and maximum response time. Italso leads to minimizing the customer satisfaction level along with cloud providers,leading to the penalty. The services hosted on VMs or Containers are highlydemanding services, and these highly demanding services are handled with the help ofload balancing. Load balancing is a way to automatically transfer the incoming requestsor load across a group of back-end containers. It improves the distribution of workloadacross multiple virtual machines. Traditionally, load balancing algorithms use one ortwo parameters to balance the load. In this paper, we used one of the popularoptimization techniques, namely the Technique for Order of Preferences by Similarityto Ideal Solution (TOPSIS) algorithm to manage the incoming traffic with the multiplecriteriadecision-making (MCDM) technique. When the proposed technique wascompared with different other techniques, such as round robin, it was found thatTOPSIS gives better performance in terms of efficient resources utilization. It alsominimizes the average response time, which prevents the machine from gettingoverloaded.

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SummaryIn today's era of fast‐growing network‐enabled devices combined, it increases the complexity of the network. This leads to the massive data packet transfer on the network via the data plane in a software‐defined networking environment. The programmable packet processing in a data plane may introduce indirect bugs that are hard to catch manually. To avoid catastrophic after‐effects, such programs need to be formally verified. Researchers have proposed various tools and techniques to verify the data plane program using the P4 language. Most of the researchers have used the concept of assertion and symbolic execution to provide P4 verification approaches. As symbolic execution does not scale up well, researchers have proposed different techniques, which include the use of constraints, slicing of the program, parallelization, data plane verification, program verification, and so on. The tools have experimented with different choices for compiler optimization. In this article, we perform a pervasive survey on various verification tools and techniques based on data plane programming using domain‐specific language like P4 from the inception of the concept. We have compared the packet processing tools developed as per the requirement of time with their ideology and the impact of change.