This set of resources available in optical clouds can be analyzed from different points of view, i.e. user, enterprise and cloud service provider. Service relocation can also be used during the recovery process of a disrupted cloud service (AHMED et al., 2014).
Proposal
Usually, the recovery process of an unprotected connection relies on the remaining unused infrastructure resources to establish a new path that replaces the failed one. Furthermore, such modifications to the recovery process do not have a significant impact on the capacity to accept requests.
Document Organization
From this point of view, academic (also called scientific), enterprise (business) or non-professional (consumer) applications can be deployed in the optical cloud (DEVELDER et al., 2013). The successful deployment of optical clouds requires the interaction of IT and network resources, and their interdependence must be addressed in order to meet the demands of cloud applications (DEVELDER et al., 2012).
Cloud Service Lifecycle
However, since optical clouds offer a higher degree of flexibility, other operations (eg, service relocation) can be used to restore service. Once the service is fully recovered, it returns to the operational phase and remains until either its retention period ends or a new failure occurs.
Anycast Traffic
In cases where a new set of resources is provided to restore the disrupted service, the restoration provision releases the previously reserved resources and reserves the new set.
Service Relocation
Live Relocation
The live migration is performed in several phases to synchronize all data during VM execution. However, if we consider the live migration of a disrupted cloud service, the VM may be disabled as no end users can access it. Another aspect of live migration that must be taken into account is that this process can only be performed if the VM is in working conditions and can be reached, i.e. if there is a network.
If the VM is not reachable on the network or if the storage is down, the live migration process becomes impossible.
Continual Migration
Restoration with Relocation
In this case, suppose the cloud service demand from node 1 is initially assigned to DC 2 via network connection (1,2) and is called service 1. Suppose that connection (1,2) has a fiber optic failure and is then no longer available, causing service 1 to be disrupted by the failure. In traditional restoration approaches, the only way to restore the disrupted service is to create a new path from the source to the destination, which is not possible given the topology.
Since there are no work paths between the source and assigned DC, the recovery process can move (ie migrate) the cloud service from DC 2 to DC 5 or 6, since both DC 5 and DC 6 have sufficient IT and network resources.
Optical Clouds Management
Performance Metrics
- Blocking probability
- Restorability
- Availability
- Relocations
𝐷𝑆 is the total number of services interrupted for fiber failures experienced during the simulation;. 𝑅𝐸 is the total number of service relocation performed between all restored services 𝑅𝑆 during the restoration phase. In simulation environments, it can be measured as the ratio of the total number of successfully restored services (𝑅𝑆) over the total number of interrupted services (𝐷𝑆), as defined in Eq.
The percentage of displacements can be calculated by Eq. 2.13), this is the ratio between the number of transfers and the number of service interruptions.
Applicability in Smart Cities
The ICT Infrastructure on Attractive Cities
In the case of the cloud computing layer, the infrastructure can be divided into storage and processing infrastructures, which can be combined to process or track requests. The IT layer can be geographically distributed in different data centers (locations containing a certain amount of IT resources) interconnected by the telecommunications layer. All collected data can be stored in the cloud computing layer using storage resources.
In the second step, the CCC requests information that can be generated using the data stored on the storage infrastructure.
Modeling Details
- Optical network
- Data centers
- Cloud service
- Infrastructure resilience
Cloud service is a concept of application that can use all the advantages of innovative ICT infrastructure (optical networks and DC). A cloud service can be any application or algorithm designed to use storage and processing capacity, and in a smart city scenario there are many examples of high capacity applications that will need to be processed in the ICT infrastructure One example is the use of weather forecasting models which uses a large amount of IT resources while processing the data to generate the forecast.
To reduce costs and changes in ICT infrastructure, resilience strategies have an important role in ICT management.
Survivability on Optical Networks
This chapter aims to show the related works and state-of-the-art in survivability topics in optical networks and optical clouds. The authors in Habib et al. 2013) present a comprehensive survey showing all concepts related to disaster resilience for optical networks. Modeling Shared Risk Groups (SRGs) capable of representing the risk of disasters in the network is a key issue among the open issues and challenges pointed out to prepare optical networks to survive the occurrence of such major failures .
The benefits of considering degraded services in the presence of multiple correlated failures in the network have been explored by (HUANG; MARTEL; MUKHERJEE, 2009).
Status Awareness
Self-organized networks and content distributions are powerful capabilities that can be used to improve network survivability during disasters. To improve network performance, several works have proposed the use of link properties. A similar approach is used in (SONG; ZHANG; MUKHERJEE, 2007), where the authors propose a dynamic provisioning strategy to route and protect each connection (if necessary).
The proposed strategy also reduces the over-building of resources in the system, which can affect the reduction of the link price.
Survivability on Optical Clouds
A similar work, (DEVELDER et al., 2012), introduces a 1: 𝑁 link to increase network and DC dimensions, improving reliability in cases where displacement is not used; and the same connection is used in (DEVELDER et al., 2013). In (DEVELDER et al., 2013) it is shown, as expected, that protecting services against server and link failures without failover and with a 1:1 protection strategy requires twice the server resources. For protecting cloud services against catastrophic failures (e.g., multiple cascading, interconnected, and collocated failures), the paper (HABIB et al., 2011) proposes an ILP model for resource assignment and content placement, i which explores the use of displacement.
The work in (HABIB et al., 2012) extends the previous work by proposing an ILP relaxation capable of reducing model complexity as well as a heuristic that achieves good performance.
Complementary Works
This work is based on the hypothesis that service relocation and differentiation can be used in the recovery process to improve the survivability of optical clouds without increasing the need for backup resources. In addition to the scenario representation, it allows the analysis of survivability in a higher number of links per failure (ie, the maximum number of broken links in a single failure is equal to the number of network devices in the broken fiber). 2014) propose an optimal model that only takes into account the dynamics of the network part of optical clouds. In other words, the changes that occur at the DCs during the solution of the model are not represented in the model.
Survivability can be improved if cloud services differentiation is taken into account in the restoration process.
Scope Definition and Assumptions
In this example, the first request that arrives requires a certain amount of resources, and this request remains in the system until the service reaches its service time or an error disrupts it. During the operation time, the cloud service is deployed by the CSP customer in the cloud and listens to the network throughout its lifetime. End users may access the Service by sending requests to the Service and waiting for responses.
End user requests, use of cloud service resources and performance are not analyzed in this work.
Path Restoration with Service Relocation and Differentiation Problem 43
Heuristic for the PR-SDR Problem
The objective of the heuristic is the same as that of the IRP, i.e. reducing the following three metrics: (i) the average downtime value of all unrestored cloud services, (ii) the number of successfully restored cloud services required. to be moved, and (iii) the number of network units used in the recovery process. For each𝑄𝑖 ∈𝑄′, the heuristic first checks whether there is a path with enough network resources in 𝐺(𝑁, 𝐸) from 𝑄𝑠𝑟𝑐𝑖 to the DC node already in use (i.e. whether there are no available network resources on any of the pre-calculated paths, this function gives 𝑁 𝑈 𝐿𝐿;.
This chapter investigates the performance of the IRP and PBNJ approaches described in the previous chapter.
Simulation Setup and Metrics
Regardless of the topology, all optical links in the network are bidirectional, with 80 network units in each direction. The amount of memory and processing units required by each cloud service is chosen uniformly in the intervals [1,100] and [1,5], respectively (AHMED et al., 2014). Unless otherwise specified, the value of Δ (ie, the transfer rate of memory units) is equal to 100 [memory_units/s].
This means that in the HRP strategy, decisions about which cloud service to restore first are based only on their respective value of the remaining service time.
Results
11 makes it possible to estimate how close the results of the HRP are to the optimal, i.e. the results of the IRP. The average performance (i.e. the IRP and HRP curves in the figure) is very close to that of. The low priority services experience a reduction of up to 9% in recoverability compared to the average case (i.e. the IRP curve), a value that is still a good improvement over the ILP_PR curve which neither moves nor processes the cloud enables prioritized services.
As explained in Section 4.2.1, the value of Δ has an impact on the relocation stop.
Contributions
This thesis proposes a recovery-based survival strategy that can be used to recover cloud services disrupted by fiber optic link failures. The former is used to improve the average service availability and recovery performance provided by a recovery-based approach. The results of the performance assessment study show that both IRP and HRP are able to improve average service availability and recoverability with a limited number of cloud service relocations compared to conventional recovery-based techniques.
In addition, thanks to service differentiation, the availability and recoverability performance of critical cloud services is very close to the one that can be achieved with a protection-based strategy, but with the inherent benefits in terms of efficient resource use resulting from a recovery. -based approach.
Future Works and Trends
The author received a scholarship from the Science without Borders program and was able to stay a year in close collaboration with KTH researchers, and such joint work continues with the publication and submission of several works related to the optical network field. The virtualization of wireless network components and the respective allocation from antenna equipment to optical clouds helps to lower antenna prices, as well as allowing flexibility to allocate resources where there are end users who need them. Providing the necessary QoS and QoE combined with high flexibility and scalability, optical clouds will play an important role.
A new algorithm for dimensioning elastic optical networks for shared mesh protection against multiple link failures.
